With complex systems such as spacecraft, we often need to achieve goals even though failures prevent the exact state of the system from being determined. Conformant planning is the problem of generating a plan that moves a system from any of a number of possible initial states to a goal state, given that actions may have uncertain outcomes and sensing is unavailable. Two existing approaches to conformant planning are to consider the effects of actions in all worlds simultaneously, or to generate a plan in one world and test it in the remaining worlds. In contrast, in this work we attempt to find a plan for one world and extend it to work in all worlds. This approach is motivated by the desire to find conformant plans when one exists and partially conformant plans when one does not. It can be implemented with many underlying planning approaches and search strategies, and can be used in an anytime manner. We show that on a familiar conformant planning domain this approach is competitive with all but the fastest planners on serial problems and dominant on problems where a parallel plan is required.

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